tft.scale_by_min_max_per_key

Scale a numerical column into a predefined range on a per-key basis.

tft.scale_by_min_max_per_key(
    x: common_types.ConsistentTensorType,
    key: common_types.TensorType,
    output_min: float = 0.0,
    output_max: float = 1.0,
    elementwise: bool = False,
    key_vocabulary_filename: Optional[str] = None,
    name: Optional[str] = None
) -> common_types.ConsistentTensorType

Args
`x`	A numeric `Tensor`, `SparseTensor`, or `RaggedTensor`.
`key`	A `Tensor`, `SparseTensor`, or `RaggedTensor` of dtype tf.string. Must meet one of the following conditions: key is None Both x and key are dense, Both x and key are composite and `key` must exactly match `x` in everything except values, The axis=1 index of each x matches its index of dense key.
`output_min`	The minimum of the range of output values.
`output_max`	The maximum of the range of output values.
`elementwise`	If true, scale each element of the tensor independently.
`key_vocabulary_filename`	(Optional) The file name for the per-key file. If None, this combiner will assume the keys fit in memory and will not store the analyzer result in a file. If '', a file name will be chosen based on the current TensorFlow scope. If not '', it should be unique within a given preprocessing function.
`name`	(Optional) A name for this operation.

Example:

def preprocessing_fn(inputs):
  return {
     'scaled': tft.scale_by_min_max_per_key(inputs['x'], inputs['s'])
  }
raw_data = [dict(x=1, s='a'), dict(x=0, s='b'), dict(x=3, s='a')]
feature_spec = dict(
    x=tf.io.FixedLenFeature([], tf.float32),
    s=tf.io.FixedLenFeature([], tf.string))
raw_data_metadata = tft.DatasetMetadata.from_feature_spec(feature_spec)
with tft_beam.Context(temp_dir=tempfile.mkdtemp()):
  transformed_dataset, transform_fn = (
      (raw_data, raw_data_metadata)
      | tft_beam.AnalyzeAndTransformDataset(preprocessing_fn))
transformed_data, transformed_metadata = transformed_dataset
transformed_data
[{'scaled': 0.0}, {'scaled': 0.5}, {'scaled': 1.0}]

Returns
A `Tensor`, `SparseTensor`, or `RaggedTensor` containing the input column scaled to [output_min, output_max] on a per-key basis if a key is provided. If the analysis dataset is empty, a certain key contains a single distinct value or the computed key vocabulary doesn't have an entry for `key`, then `x` is scaled using a sigmoid function.

Raises
`ValueError`	If output_min, output_max have the wrong order.
`NotImplementedError`	If elementwise is True and key is not None.
`InvalidArgumentError`	If indices of sparse x and key do not match.

tft.scale_by_min_max_per_key

Args

Example:

Returns

Raises